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Research Data Management


This guide is intended to support the NUS community in effective research data management throughout the data lifecycle of data planning, documenting, storage, sharing, and long-term preservation. This guide is collaboratively developed by NUS, NTU and SMU Libraries.

Catch our Researcher Unbound session from 18 Oct 2023 where we had an insightful conversation with Prof Kan Min-Yen (NUS Computing) on his data sharing journey and the benefits of doing good Research Data Management!


Some RDM insights shared by Prof Kan:

[16:34] “When you share data... and you (describe) your data well, then it becomes much more discoverable for others… And that discovery is key if you want to promote impact.”

[16:03] “When we are not very experienced with data sharing, there's a little bit of an adoption curve that you have to surmount… so you have to prepare the metadata, and once your research group… gets sort of accustomed to it, (it’s) just sort of like filling out institutional review forms… which is absolutely necessary for a lot of research.”

[17:30] “When you're thinking about sharing your data, you really have to think about who is going to use it… and then try to visualise what types of tags or metadata would I be searching for… and then insert them into (a) system like ScholarBank, so that when a search engine like Google or another search engine is able to find that information, it can be reported (and retrieved).”

[34:11] “For people and groups that are not doing data sharing, it requires a little bit of forward thinking and to say, you know, is it possible that by sharing data, my group is going to create more impact overall... because the way we create impact is not (just) to create work, it's to create work that's being used by others.”

What is Research Data Management

Research Data Management (RDM) is "how you look after your data throughout your project. It covers the planning, collecting, organising, managing, storage, security, backing up, preserving, and sharing your data and ensures that research data are managed according to legal, statutory, ethical and funding body requirements" (Whyte, A. & Tedds, J., 2011).

RDM occurs in every stage of the research lifecycle, from before you start a research project when you are doing a Data Management Plan, and continues well after your project has concluded with the publication of your research paper.

Adapted from:

What is Research Data

Here are some of the recognised definitions of research data:

"Research data, unlike other types of information, is collected, observed, or created, for purposes of analysis to produce original research results." Edinburgh University Data Library Research Data Management Handbook  

“Research data means data in the form of facts, observations, images, computer program results, recordings, measurements or experiences on which an argument, theory, test or hypothesis, or another research output is based. Data may be numerical, descriptive, visual or tactile. It may be raw, cleaned or processed, and may be held in any format or media”. The Queensland University of Technology Management of Research Data Policy

“The recorded information (regardless of the form or the media in which they may exist) necessary to support or validate a research project’s observations, findings or outputs”. The University of Oxford Policy on Management of Research Data and Records

In addition to research data, research data management also covers managing of research records both during and beyond the life of a project. Examples of such research records include:

  • Correspondence (electronic mail and paper-based correspondence)
  • Project files
  • Grant applications
  • Ethics applications
  • Technical reports
  • Research reports
  • Signed consent forms

Source: MANTRA

Why Manage Research Data

Research data represents significant value to researchers and the University, and good stewardship of research data is necessary to validate the outcomes and maintain the integrity of research results.

  • Ensuring research integrity and reproducibility
  • Ensuring research data and records are accurate, complete, authentic and reliable
  • Complying with practices conducted in industry and commerce
  • Meeting funding body grant requirements (if applicable)
  • Enhancing data security and minimising the risk of data loss
  • Saving time and resources in the long run
  • Preventing duplication of effort by enabling others to use your data
  • Facilitating the analysis of change, by providing data with which data at other points in time can be compared

Source: MANTRA